Impact of the Query Set on the Evaluation of Expert Finding Systems
This work addresses evaluation challenges for expert finding systems, but it is incremental as it focuses on methodology rather than new algorithms.
The paper tackled the problem of evaluating expert finding systems by comparing topic-based queries with a new document-query methodology, showing that using more realistic queries leads to different evaluation results.
Expertise is a loosely defined concept that is hard to formalize. Much research has focused on designing efficient algorithms for expert finding in large databases in various application domains. The evaluation of such recommender systems lies most of the time on human-annotated sets of experts associated with topics. The protocol of evaluation consists in using the namings or short descriptions of these topics as raw queries in order to rank the available set of candidates. Several measures taken from the field of information retrieval are then applied to rate the rankings of candidates against the ground truth set of experts. In this paper, we apply this topic-query evaluation methodology with the AMiner data and explore a new document-query methodology to evaluate experts retrieval from a set of queries sampled directly from the experts documents. Specifically, we describe two datasets extracted from AMiner, three baseline algorithms from the literature based on several document representations and provide experiment results to show that using a wide range of more realistic queries provides different evaluation results to the usual topic-queries.